{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d418ef04",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from lightgbm import LGBMClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "943820c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, confusion_matrix, f1_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "25cb31ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID_REF</th>\n",
       "      <th>GSM1077598</th>\n",
       "      <th>GSM1077599</th>\n",
       "      <th>GSM1077600</th>\n",
       "      <th>GSM1077601</th>\n",
       "      <th>GSM1077602</th>\n",
       "      <th>GSM1077603</th>\n",
       "      <th>GSM1077604</th>\n",
       "      <th>GSM1077605</th>\n",
       "      <th>GSM1077606</th>\n",
       "      <th>...</th>\n",
       "      <th>GSM1077834</th>\n",
       "      <th>GSM1077835</th>\n",
       "      <th>GSM1077836</th>\n",
       "      <th>GSM1077837</th>\n",
       "      <th>GSM1077838</th>\n",
       "      <th>GSM1077839</th>\n",
       "      <th>GSM1077840</th>\n",
       "      <th>GSM1077841</th>\n",
       "      <th>GSM1077842</th>\n",
       "      <th>GSM1077843</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11715100_at</td>\n",
       "      <td>3.6599</td>\n",
       "      <td>2.5712</td>\n",
       "      <td>3.3174</td>\n",
       "      <td>3.1835</td>\n",
       "      <td>2.9949</td>\n",
       "      <td>3.4481</td>\n",
       "      <td>3.1692</td>\n",
       "      <td>3.5142</td>\n",
       "      <td>2.7073</td>\n",
       "      <td>...</td>\n",
       "      <td>3.2312</td>\n",
       "      <td>4.4599</td>\n",
       "      <td>3.8468</td>\n",
       "      <td>3.8416</td>\n",
       "      <td>4.2620</td>\n",
       "      <td>2.7624</td>\n",
       "      <td>2.5775</td>\n",
       "      <td>3.1441</td>\n",
       "      <td>4.8256</td>\n",
       "      <td>3.3535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11715101_s_at</td>\n",
       "      <td>4.1524</td>\n",
       "      <td>4.1879</td>\n",
       "      <td>4.1040</td>\n",
       "      <td>4.3635</td>\n",
       "      <td>4.5227</td>\n",
       "      <td>4.1203</td>\n",
       "      <td>4.2486</td>\n",
       "      <td>4.1462</td>\n",
       "      <td>3.8748</td>\n",
       "      <td>...</td>\n",
       "      <td>3.8795</td>\n",
       "      <td>5.7232</td>\n",
       "      <td>4.7099</td>\n",
       "      <td>4.0111</td>\n",
       "      <td>4.9795</td>\n",
       "      <td>3.7898</td>\n",
       "      <td>3.7737</td>\n",
       "      <td>4.3357</td>\n",
       "      <td>5.4895</td>\n",
       "      <td>4.4803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11715102_x_at</td>\n",
       "      <td>3.3896</td>\n",
       "      <td>3.1228</td>\n",
       "      <td>3.2434</td>\n",
       "      <td>3.1175</td>\n",
       "      <td>3.3627</td>\n",
       "      <td>2.8233</td>\n",
       "      <td>3.4594</td>\n",
       "      <td>3.1960</td>\n",
       "      <td>3.1082</td>\n",
       "      <td>...</td>\n",
       "      <td>3.3990</td>\n",
       "      <td>5.1110</td>\n",
       "      <td>4.2058</td>\n",
       "      <td>3.2759</td>\n",
       "      <td>4.4833</td>\n",
       "      <td>3.0972</td>\n",
       "      <td>2.7807</td>\n",
       "      <td>3.4050</td>\n",
       "      <td>4.4170</td>\n",
       "      <td>3.2669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11715103_x_at</td>\n",
       "      <td>3.8929</td>\n",
       "      <td>3.7299</td>\n",
       "      <td>4.1048</td>\n",
       "      <td>3.3426</td>\n",
       "      <td>3.3376</td>\n",
       "      <td>3.0590</td>\n",
       "      <td>3.2893</td>\n",
       "      <td>3.2290</td>\n",
       "      <td>2.9375</td>\n",
       "      <td>...</td>\n",
       "      <td>3.7911</td>\n",
       "      <td>3.1532</td>\n",
       "      <td>4.2702</td>\n",
       "      <td>3.4329</td>\n",
       "      <td>3.5422</td>\n",
       "      <td>4.3038</td>\n",
       "      <td>3.2338</td>\n",
       "      <td>3.4187</td>\n",
       "      <td>4.6358</td>\n",
       "      <td>4.1998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11715104_s_at</td>\n",
       "      <td>7.3799</td>\n",
       "      <td>7.7642</td>\n",
       "      <td>6.4705</td>\n",
       "      <td>7.0046</td>\n",
       "      <td>6.9029</td>\n",
       "      <td>6.1424</td>\n",
       "      <td>6.5051</td>\n",
       "      <td>6.4659</td>\n",
       "      <td>6.6958</td>\n",
       "      <td>...</td>\n",
       "      <td>2.6693</td>\n",
       "      <td>2.6769</td>\n",
       "      <td>2.7843</td>\n",
       "      <td>2.6698</td>\n",
       "      <td>2.8176</td>\n",
       "      <td>3.0904</td>\n",
       "      <td>2.6377</td>\n",
       "      <td>3.0226</td>\n",
       "      <td>2.7754</td>\n",
       "      <td>2.3838</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 247 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          ID_REF  GSM1077598  GSM1077599  GSM1077600  GSM1077601  GSM1077602  \\\n",
       "0    11715100_at      3.6599      2.5712      3.3174      3.1835      2.9949   \n",
       "1  11715101_s_at      4.1524      4.1879      4.1040      4.3635      4.5227   \n",
       "2  11715102_x_at      3.3896      3.1228      3.2434      3.1175      3.3627   \n",
       "3  11715103_x_at      3.8929      3.7299      4.1048      3.3426      3.3376   \n",
       "4  11715104_s_at      7.3799      7.7642      6.4705      7.0046      6.9029   \n",
       "\n",
       "   GSM1077603  GSM1077604  GSM1077605  GSM1077606  ...  GSM1077834  \\\n",
       "0      3.4481      3.1692      3.5142      2.7073  ...      3.2312   \n",
       "1      4.1203      4.2486      4.1462      3.8748  ...      3.8795   \n",
       "2      2.8233      3.4594      3.1960      3.1082  ...      3.3990   \n",
       "3      3.0590      3.2893      3.2290      2.9375  ...      3.7911   \n",
       "4      6.1424      6.5051      6.4659      6.6958  ...      2.6693   \n",
       "\n",
       "   GSM1077835  GSM1077836  GSM1077837  GSM1077838  GSM1077839  GSM1077840  \\\n",
       "0      4.4599      3.8468      3.8416      4.2620      2.7624      2.5775   \n",
       "1      5.7232      4.7099      4.0111      4.9795      3.7898      3.7737   \n",
       "2      5.1110      4.2058      3.2759      4.4833      3.0972      2.7807   \n",
       "3      3.1532      4.2702      3.4329      3.5422      4.3038      3.2338   \n",
       "4      2.6769      2.7843      2.6698      2.8176      3.0904      2.6377   \n",
       "\n",
       "   GSM1077841  GSM1077842  GSM1077843  \n",
       "0      3.1441      4.8256      3.3535  \n",
       "1      4.3357      5.4895      4.4803  \n",
       "2      3.4050      4.4170      3.2669  \n",
       "3      3.4187      4.6358      4.1998  \n",
       "4      3.0226      2.7754      2.3838  \n",
       "\n",
       "[5 rows x 247 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#데이터 불러오기\n",
    "dataset = pd.read_csv('./colon_cancer_dataset.csv')\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7b390f4e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 246 entries, GSM1077598 to GSM1077843\n",
      "Columns: 49386 entries, 11715100_at to AFFX-TrpnX-M_at\n",
      "dtypes: object(49386)\n",
      "memory usage: 92.7+ MB\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>11715100_at</th>\n",
       "      <th>11715101_s_at</th>\n",
       "      <th>11715102_x_at</th>\n",
       "      <th>11715103_x_at</th>\n",
       "      <th>11715104_s_at</th>\n",
       "      <th>11715105_at</th>\n",
       "      <th>11715106_x_at</th>\n",
       "      <th>11715107_s_at</th>\n",
       "      <th>11715108_x_at</th>\n",
       "      <th>11715109_at</th>\n",
       "      <th>...</th>\n",
       "      <th>AFFX-r2-TagO-3_at</th>\n",
       "      <th>AFFX-r2-TagO-5_at</th>\n",
       "      <th>AFFX-r2-TagQ-3_at</th>\n",
       "      <th>AFFX-r2-TagQ-5_at</th>\n",
       "      <th>AFFX-ThrX-3_at</th>\n",
       "      <th>AFFX-ThrX-5_at</th>\n",
       "      <th>AFFX-ThrX-M_at</th>\n",
       "      <th>AFFX-TrpnX-3_at</th>\n",
       "      <th>AFFX-TrpnX-5_at</th>\n",
       "      <th>AFFX-TrpnX-M_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GSM1077598</th>\n",
       "      <td>3.6599</td>\n",
       "      <td>4.1524</td>\n",
       "      <td>3.3896</td>\n",
       "      <td>3.8929</td>\n",
       "      <td>7.3799</td>\n",
       "      <td>2.0732</td>\n",
       "      <td>2.8276</td>\n",
       "      <td>2.6345</td>\n",
       "      <td>2.5165</td>\n",
       "      <td>2.4795</td>\n",
       "      <td>...</td>\n",
       "      <td>1.9475</td>\n",
       "      <td>2.1732</td>\n",
       "      <td>2.3173</td>\n",
       "      <td>2.0892</td>\n",
       "      <td>7.0313</td>\n",
       "      <td>4.8902</td>\n",
       "      <td>5.9995</td>\n",
       "      <td>2.1907</td>\n",
       "      <td>2.1474</td>\n",
       "      <td>2.1102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GSM1077599</th>\n",
       "      <td>2.5712</td>\n",
       "      <td>4.1879</td>\n",
       "      <td>3.1228</td>\n",
       "      <td>3.7299</td>\n",
       "      <td>7.7642</td>\n",
       "      <td>2.3151</td>\n",
       "      <td>2.8944</td>\n",
       "      <td>2.402</td>\n",
       "      <td>2.5873</td>\n",
       "      <td>2.2066</td>\n",
       "      <td>...</td>\n",
       "      <td>1.9056</td>\n",
       "      <td>1.9503</td>\n",
       "      <td>2.0665</td>\n",
       "      <td>2.0585</td>\n",
       "      <td>7.3201</td>\n",
       "      <td>5.2443</td>\n",
       "      <td>6.2646</td>\n",
       "      <td>1.9371</td>\n",
       "      <td>2.2677</td>\n",
       "      <td>2.3093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GSM1077600</th>\n",
       "      <td>3.3174</td>\n",
       "      <td>4.104</td>\n",
       "      <td>3.2434</td>\n",
       "      <td>4.1048</td>\n",
       "      <td>6.4705</td>\n",
       "      <td>2.1824</td>\n",
       "      <td>2.7407</td>\n",
       "      <td>2.4271</td>\n",
       "      <td>2.5785</td>\n",
       "      <td>2.6003</td>\n",
       "      <td>...</td>\n",
       "      <td>2.507</td>\n",
       "      <td>2.4232</td>\n",
       "      <td>2.4662</td>\n",
       "      <td>2.2127</td>\n",
       "      <td>8.027</td>\n",
       "      <td>5.814</td>\n",
       "      <td>6.3628</td>\n",
       "      <td>2.1923</td>\n",
       "      <td>2.2233</td>\n",
       "      <td>2.2221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GSM1077601</th>\n",
       "      <td>3.1835</td>\n",
       "      <td>4.3635</td>\n",
       "      <td>3.1175</td>\n",
       "      <td>3.3426</td>\n",
       "      <td>7.0046</td>\n",
       "      <td>2.1961</td>\n",
       "      <td>2.471</td>\n",
       "      <td>2.2932</td>\n",
       "      <td>2.3527</td>\n",
       "      <td>2.4741</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0228</td>\n",
       "      <td>2.7202</td>\n",
       "      <td>2.4453</td>\n",
       "      <td>2.3259</td>\n",
       "      <td>7.1081</td>\n",
       "      <td>4.7067</td>\n",
       "      <td>5.1356</td>\n",
       "      <td>2.0068</td>\n",
       "      <td>2.2773</td>\n",
       "      <td>2.2656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GSM1077602</th>\n",
       "      <td>2.9949</td>\n",
       "      <td>4.5227</td>\n",
       "      <td>3.3627</td>\n",
       "      <td>3.3376</td>\n",
       "      <td>6.9029</td>\n",
       "      <td>2.5087</td>\n",
       "      <td>2.8609</td>\n",
       "      <td>2.4833</td>\n",
       "      <td>2.3683</td>\n",
       "      <td>2.3747</td>\n",
       "      <td>...</td>\n",
       "      <td>1.9309</td>\n",
       "      <td>2.1284</td>\n",
       "      <td>2.6587</td>\n",
       "      <td>2.4271</td>\n",
       "      <td>7.7353</td>\n",
       "      <td>5.2432</td>\n",
       "      <td>6.1207</td>\n",
       "      <td>2.1847</td>\n",
       "      <td>2.303</td>\n",
       "      <td>2.1275</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 49386 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           11715100_at 11715101_s_at 11715102_x_at 11715103_x_at  \\\n",
       "GSM1077598      3.6599        4.1524        3.3896        3.8929   \n",
       "GSM1077599      2.5712        4.1879        3.1228        3.7299   \n",
       "GSM1077600      3.3174         4.104        3.2434        4.1048   \n",
       "GSM1077601      3.1835        4.3635        3.1175        3.3426   \n",
       "GSM1077602      2.9949        4.5227        3.3627        3.3376   \n",
       "\n",
       "           11715104_s_at 11715105_at 11715106_x_at 11715107_s_at  \\\n",
       "GSM1077598        7.3799      2.0732        2.8276        2.6345   \n",
       "GSM1077599        7.7642      2.3151        2.8944         2.402   \n",
       "GSM1077600        6.4705      2.1824        2.7407        2.4271   \n",
       "GSM1077601        7.0046      2.1961         2.471        2.2932   \n",
       "GSM1077602        6.9029      2.5087        2.8609        2.4833   \n",
       "\n",
       "           11715108_x_at 11715109_at  ... AFFX-r2-TagO-3_at AFFX-r2-TagO-5_at  \\\n",
       "GSM1077598        2.5165      2.4795  ...            1.9475            2.1732   \n",
       "GSM1077599        2.5873      2.2066  ...            1.9056            1.9503   \n",
       "GSM1077600        2.5785      2.6003  ...             2.507            2.4232   \n",
       "GSM1077601        2.3527      2.4741  ...            2.0228            2.7202   \n",
       "GSM1077602        2.3683      2.3747  ...            1.9309            2.1284   \n",
       "\n",
       "           AFFX-r2-TagQ-3_at AFFX-r2-TagQ-5_at AFFX-ThrX-3_at AFFX-ThrX-5_at  \\\n",
       "GSM1077598            2.3173            2.0892         7.0313         4.8902   \n",
       "GSM1077599            2.0665            2.0585         7.3201         5.2443   \n",
       "GSM1077600            2.4662            2.2127          8.027          5.814   \n",
       "GSM1077601            2.4453            2.3259         7.1081         4.7067   \n",
       "GSM1077602            2.6587            2.4271         7.7353         5.2432   \n",
       "\n",
       "           AFFX-ThrX-M_at AFFX-TrpnX-3_at AFFX-TrpnX-5_at AFFX-TrpnX-M_at  \n",
       "GSM1077598         5.9995          2.1907          2.1474          2.1102  \n",
       "GSM1077599         6.2646          1.9371          2.2677          2.3093  \n",
       "GSM1077600         6.3628          2.1923          2.2233          2.2221  \n",
       "GSM1077601         5.1356          2.0068          2.2773          2.2656  \n",
       "GSM1077602         6.1207          2.1847           2.303          2.1275  \n",
       "\n",
       "[5 rows x 49386 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = dataset.transpose()\n",
    "dataset = dataset.rename(columns=dataset.iloc[0])\n",
    "dataset.drop(dataset.index[0], inplace=True)\n",
    "dataset.drop(dataset.columns[-1], axis=1, inplace = True)\n",
    "dataset.isnull().sum().sum()\n",
    "dataset.info()\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "812bc922",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = dataset.astype(float)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "074e7e91",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 246 entries, GSM1077598 to GSM1077843\n",
      "Columns: 49386 entries, 11715100_at to AFFX-TrpnX-M_at\n",
      "dtypes: float64(49386)\n",
      "memory usage: 92.7+ MB\n"
     ]
    }
   ],
   "source": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9cbfc775",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            11715100_at  11715101_s_at  11715102_x_at  11715103_x_at  \\\n",
      "GSM1077709       3.2254         3.9726         3.5545         3.3030   \n",
      "GSM1077800       2.3269         3.1299         2.5957         3.2958   \n",
      "GSM1077671       3.2287         3.9501         3.4721         3.2566   \n",
      "GSM1077706       3.3393         3.3633         2.9115         3.7855   \n",
      "GSM1077787       4.3524         4.2284         3.5407         4.0740   \n",
      "GSM1077654       3.3437         4.1059         3.4619         3.1868   \n",
      "GSM1077703       3.0975         3.6471         3.1603         3.3734   \n",
      "GSM1077699       2.7608         4.1047         3.2845         3.7064   \n",
      "GSM1077658       3.9125         4.6021         3.5206         3.5056   \n",
      "GSM1077697       2.7018         3.6255         2.9101         3.6305   \n",
      "\n",
      "            11715104_s_at  11715105_at  11715106_x_at  11715107_s_at  \\\n",
      "GSM1077709         7.7440       2.2524         3.1468         2.5168   \n",
      "GSM1077800         2.6036       2.2401         2.4668         2.0852   \n",
      "GSM1077671         6.7975       2.4250         2.5807         2.1609   \n",
      "GSM1077706         6.6524       2.1066         2.4650         2.1077   \n",
      "GSM1077787         3.0757       2.3612         2.7389         2.3363   \n",
      "GSM1077654         5.0876       2.2426         2.6077         1.9452   \n",
      "GSM1077703         7.0975       2.2761         2.5053         2.3739   \n",
      "GSM1077699         7.0808       2.1444         2.6933         2.1907   \n",
      "GSM1077658         4.1768       2.4471         3.0749         2.7334   \n",
      "GSM1077697         7.0516       1.9951         2.7566         2.2714   \n",
      "\n",
      "            11715108_x_at  11715109_at  ...  AFFX-r2-TagO-5_at  \\\n",
      "GSM1077709         2.4655       2.2479  ...             2.2437   \n",
      "GSM1077800         2.1384       2.1339  ...             2.5546   \n",
      "GSM1077671         2.3651       2.7595  ...             2.2426   \n",
      "GSM1077706         2.1625       2.2038  ...             2.1475   \n",
      "GSM1077787         2.3853       2.4086  ...             2.4073   \n",
      "GSM1077654         2.1596       2.4215  ...             2.3229   \n",
      "GSM1077703         2.2696       2.6394  ...             2.2236   \n",
      "GSM1077699         1.9680       2.3893  ...             2.2006   \n",
      "GSM1077658         2.6272       2.1478  ...             2.3996   \n",
      "GSM1077697         2.4097       2.2990  ...             2.0862   \n",
      "\n",
      "            AFFX-r2-TagQ-3_at  AFFX-r2-TagQ-5_at  AFFX-ThrX-3_at  \\\n",
      "GSM1077709             2.2278             2.1566          7.4935   \n",
      "GSM1077800             1.9642             2.1776          7.1620   \n",
      "GSM1077671             2.2736             2.4218          7.4775   \n",
      "GSM1077706             2.1595             2.0055          6.5388   \n",
      "GSM1077787             2.3177             2.3476          7.5559   \n",
      "GSM1077654             2.4396             2.0806          7.8282   \n",
      "GSM1077703             2.3987             2.4381          8.3215   \n",
      "GSM1077699             2.2277             2.1096          6.8917   \n",
      "GSM1077658             2.5391             2.0147          7.9874   \n",
      "GSM1077697             2.3128             1.9581          7.2509   \n",
      "\n",
      "            AFFX-ThrX-5_at  AFFX-ThrX-M_at  AFFX-TrpnX-3_at  AFFX-TrpnX-5_at  \\\n",
      "GSM1077709          5.3181          6.3540           2.0650           2.1882   \n",
      "GSM1077800          4.1875          5.6293           1.9928           2.1407   \n",
      "GSM1077671          4.9607          6.1917           1.8513           2.0805   \n",
      "GSM1077706          3.9241          5.0470           1.8836           1.9789   \n",
      "GSM1077787          5.5437          6.3777           2.0325           2.0663   \n",
      "GSM1077654          5.8724          6.7007           1.9762           2.0629   \n",
      "GSM1077703          6.0378          6.9529           2.0793           2.4121   \n",
      "GSM1077699          4.3387          5.4222           1.9344           2.0881   \n",
      "GSM1077658          4.6119          6.3193           2.0156           2.2043   \n",
      "GSM1077697          4.4961          5.9198           1.8782           2.1770   \n",
      "\n",
      "            AFFX-TrpnX-M_at  label  \n",
      "GSM1077709           2.1137      1  \n",
      "GSM1077800           2.1634      1  \n",
      "GSM1077671           2.2562      1  \n",
      "GSM1077706           1.9760      1  \n",
      "GSM1077787           2.2785      1  \n",
      "GSM1077654           2.1717      1  \n",
      "GSM1077703           2.3661      1  \n",
      "GSM1077699           2.2041      1  \n",
      "GSM1077658           2.2201      1  \n",
      "GSM1077697           2.1592      1  \n",
      "\n",
      "[10 rows x 49387 columns]\n"
     ]
    }
   ],
   "source": [
    "dataset['label'] = 0\n",
    "label=dataset['label'].copy()\n",
    "label[50:] = 1\n",
    "dataset['label'] = label\n",
    "print(dataset.sample(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a38b910c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    196\n",
       "0     50\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#각각의 label이 몇 개의 데이터로 이루어져있는지 확인\n",
    "dataset['label'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e081d01f",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            11715100_at  11715101_s_at  11715102_x_at  11715103_x_at  \\\n",
      "GSM1077598       3.6599         4.1524         3.3896         3.8929   \n",
      "GSM1077599       2.5712         4.1879         3.1228         3.7299   \n",
      "GSM1077600       3.3174         4.1040         3.2434         4.1048   \n",
      "GSM1077601       3.1835         4.3635         3.1175         3.3426   \n",
      "GSM1077602       2.9949         4.5227         3.3627         3.3376   \n",
      "...                 ...            ...            ...            ...   \n",
      "GSM1077839       2.7624         3.7898         3.0972         4.3038   \n",
      "GSM1077840       2.5775         3.7737         2.7807         3.2338   \n",
      "GSM1077841       3.1441         4.3357         3.4050         3.4187   \n",
      "GSM1077842       4.8256         5.4895         4.4170         4.6358   \n",
      "GSM1077843       3.3535         4.4803         3.2669         4.1998   \n",
      "\n",
      "            11715104_s_at  11715105_at  11715106_x_at  11715107_s_at  \\\n",
      "GSM1077598         7.3799       2.0732         2.8276         2.6345   \n",
      "GSM1077599         7.7642       2.3151         2.8944         2.4020   \n",
      "GSM1077600         6.4705       2.1824         2.7407         2.4271   \n",
      "GSM1077601         7.0046       2.1961         2.4710         2.2932   \n",
      "GSM1077602         6.9029       2.5087         2.8609         2.4833   \n",
      "...                   ...          ...            ...            ...   \n",
      "GSM1077839         3.0904       2.2015         2.5046         2.4100   \n",
      "GSM1077840         2.6377       2.0804         2.8766         2.3377   \n",
      "GSM1077841         3.0226       2.1792         2.7697         2.4215   \n",
      "GSM1077842         2.7754       2.3337         2.5593         2.2573   \n",
      "GSM1077843         2.3838       2.1418         2.5162         2.5197   \n",
      "\n",
      "            11715108_x_at  11715109_at  ...  AFFX-r2-TagO-3_at  \\\n",
      "GSM1077598         2.5165       2.4795  ...             1.9475   \n",
      "GSM1077599         2.5873       2.2066  ...             1.9056   \n",
      "GSM1077600         2.5785       2.6003  ...             2.5070   \n",
      "GSM1077601         2.3527       2.4741  ...             2.0228   \n",
      "GSM1077602         2.3683       2.3747  ...             1.9309   \n",
      "...                   ...          ...  ...                ...   \n",
      "GSM1077839         2.2480       2.2049  ...             1.8331   \n",
      "GSM1077840         2.0657       2.1301  ...             2.1370   \n",
      "GSM1077841         2.0079       2.2371  ...             1.8654   \n",
      "GSM1077842         2.2925       2.2268  ...             1.9590   \n",
      "GSM1077843         2.2005       2.1254  ...             1.7862   \n",
      "\n",
      "            AFFX-r2-TagO-5_at  AFFX-r2-TagQ-3_at  AFFX-r2-TagQ-5_at  \\\n",
      "GSM1077598             2.1732             2.3173             2.0892   \n",
      "GSM1077599             1.9503             2.0665             2.0585   \n",
      "GSM1077600             2.4232             2.4662             2.2127   \n",
      "GSM1077601             2.7202             2.4453             2.3259   \n",
      "GSM1077602             2.1284             2.6587             2.4271   \n",
      "...                       ...                ...                ...   \n",
      "GSM1077839             2.3223             2.3968             2.1180   \n",
      "GSM1077840             2.0858             2.2515             2.0265   \n",
      "GSM1077841             2.4228             2.1907             2.2586   \n",
      "GSM1077842             2.4964             2.0703             2.1757   \n",
      "GSM1077843             2.3495             2.7023             1.8644   \n",
      "\n",
      "            AFFX-ThrX-3_at  AFFX-ThrX-5_at  AFFX-ThrX-M_at  AFFX-TrpnX-3_at  \\\n",
      "GSM1077598          7.0313          4.8902          5.9995           2.1907   \n",
      "GSM1077599          7.3201          5.2443          6.2646           1.9371   \n",
      "GSM1077600          8.0270          5.8140          6.3628           2.1923   \n",
      "GSM1077601          7.1081          4.7067          5.1356           2.0068   \n",
      "GSM1077602          7.7353          5.2432          6.1207           2.1847   \n",
      "...                    ...             ...             ...              ...   \n",
      "GSM1077839          6.9047          4.9615          5.9032           2.0684   \n",
      "GSM1077840          7.0130          4.9894          5.8160           1.9110   \n",
      "GSM1077841          6.4545          4.3336          5.1489           2.1146   \n",
      "GSM1077842          6.9920          4.9551          6.0431           2.0207   \n",
      "GSM1077843          6.9709          4.3106          5.6033           2.0328   \n",
      "\n",
      "            AFFX-TrpnX-5_at  AFFX-TrpnX-M_at  \n",
      "GSM1077598           2.1474           2.1102  \n",
      "GSM1077599           2.2677           2.3093  \n",
      "GSM1077600           2.2233           2.2221  \n",
      "GSM1077601           2.2773           2.2656  \n",
      "GSM1077602           2.3030           2.1275  \n",
      "...                     ...              ...  \n",
      "GSM1077839           1.9758           2.2050  \n",
      "GSM1077840           2.1660           2.0183  \n",
      "GSM1077841           2.0619           2.0312  \n",
      "GSM1077842           2.2717           2.2361  \n",
      "GSM1077843           2.2460           2.1781  \n",
      "\n",
      "[246 rows x 49386 columns]\n",
      "GSM1077598    0\n",
      "GSM1077599    0\n",
      "GSM1077600    0\n",
      "GSM1077601    0\n",
      "GSM1077602    0\n",
      "             ..\n",
      "GSM1077839    1\n",
      "GSM1077840    1\n",
      "GSM1077841    1\n",
      "GSM1077842    1\n",
      "GSM1077843    1\n",
      "Name: label, Length: 246, dtype: int64\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 246 entries, GSM1077598 to GSM1077843\n",
      "Columns: 49387 entries, 11715100_at to label\n",
      "dtypes: float64(49386), int64(1)\n",
      "memory usage: 92.7+ MB\n"
     ]
    }
   ],
   "source": [
    "X = dataset.drop(['label'], axis=1)\n",
    "y = dataset['label']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size = 0.2 )\n",
    "print(X)\n",
    "print(y)\n",
    "dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "710c008d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1    139\n",
      "0     33\n",
      "Name: label, dtype: int64\n",
      "SMOTE 적용 전 학습용 피처/레이블 데이터 세트:  (172, 49386) (172,)\n",
      "SMOTE 적용 후 학습용 피처/레이블 데이터 세트:  (278, 49386) (278,)\n",
      "1    139\n",
      "0    139\n",
      "Name: label, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#SMOTE 오버샘플링 적용\n",
    "\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "X = dataset.iloc[:,:-1]\n",
    "y = dataset.iloc[:,-1]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3)\n",
    "print(y_train.value_counts())\n",
    "smote = SMOTE()\n",
    "X_train_over, y_train_over = smote.fit_resample(X_train, y_train)\n",
    "print('SMOTE 적용 전 학습용 피처/레이블 데이터 세트: ', X_train.shape, y_train.shape)\n",
    "print('SMOTE 적용 후 학습용 피처/레이블 데이터 세트: ', X_train_over.shape, y_train_over.shape)\n",
    "print(y_train_over.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2a8ce223",
   "metadata": {},
   "outputs": [],
   "source": [
    "#랜덤 포레스트 예측\n",
    "rf_clf = RandomForestClassifier(n_estimators = 1000, max_depth = 100)\n",
    "rf_clf.fit(X_train_over, y_train_over)\n",
    "pred = rf_clf.predict(X_test)\n",
    "accuracy = accuracy_score(y_test, pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b2d251dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy :  1.0\n"
     ]
    }
   ],
   "source": [
    "print('accuracy : ', accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2d3452cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "Index: 20 entries, 11749905_a_at to 11749852_s_at\n",
      "Series name: None\n",
      "Non-Null Count  Dtype  \n",
      "--------------  -----  \n",
      "20 non-null     float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 876.0+ bytes\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "#feature importance 분석\n",
    "ftr_importances_values = rf_clf.feature_importances_\n",
    "ftr_importances = pd.Series(ftr_importances_values, index = X_train_over.columns)\n",
    "ftr_top20 = ftr_importances.sort_values(ascending=False)[:20]\n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "sns.barplot(x=ftr_top20, y=ftr_top20.index)\n",
    "plt.show()\n",
    "print(ftr_top20.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "43b0f04a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1728x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset_0 = dataset[dataset['label']==0]\n",
    "dataset_1 = dataset[dataset['label']==1]\n",
    "fig, axs = plt.subplots(figsize=(24,10), nrows=1, ncols = 2)\n",
    "fig.tight_layout()\n",
    "sns.histplot(dataset_0['11743521_s_at'], kde=True, ax = axs[0])\n",
    "sns.histplot(dataset_1['11743521_s_at'], kde=True, ax = axs[1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "743cada7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1728x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset_0 = dataset[dataset['label']==0]\n",
    "dataset_1 = dataset[dataset['label']==1]\n",
    "fig, axs = plt.subplots(figsize=(24,10), nrows=1, ncols = 2)\n",
    "fig.tight_layout()\n",
    "sns.histplot(dataset_0['11717862_x_at'], kde=True, ax = axs[0])\n",
    "sns.histplot(dataset_1['11717862_x_at'], kde=True, ax = axs[1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dac67b41",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3f1fdce9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
